Overview

Dataset statistics

Number of variables38
Number of observations147026
Missing cells2653
Missing cells (%)< 0.1%
Duplicate rows3838
Duplicate rows (%)2.6%
Total size in memory42.6 MiB
Average record size in memory304.0 B

Variable types

Numeric6
Categorical32

Alerts

Dataset has 3838 (2.6%) duplicate rowsDuplicates
Age is highly overall correlated with EL_primary and 7 other fieldsHigh correlation
EL_higherprofessional_university is highly overall correlated with EL_secondary_higherHigh correlation
EL_primary is highly overall correlated with Age and 1 other fieldsHigh correlation
EL_secondary_higher is highly overall correlated with EL_higherprofessional_universityHigh correlation
Ethn_dutch is highly overall correlated with Ethn_nonwestern and 1 other fieldsHigh correlation
Ethn_nonwestern is highly overall correlated with Ethn_dutchHigh correlation
Ethn_western is highly overall correlated with Ethn_dutchHigh correlation
HHC_couple is highly overall correlated with Age and 1 other fieldsHigh correlation
HHC_couple_with_children is highly overall correlated with Age and 1 other fieldsHigh correlation
Main_moti_sparetime is highly overall correlated with Main_moti_workHigh correlation
Main_moti_work is highly overall correlated with Main_moti_sparetimeHigh correlation
Moti_sparetime is highly overall correlated with Moti_workHigh correlation
Moti_work is highly overall correlated with Moti_sparetimeHigh correlation
PW_no is highly overall correlated with Age and 3 other fieldsHigh correlation
PW_yesmorethan30h is highly overall correlated with Age and 2 other fieldsHigh correlation
UO_benefits is highly overall correlated with Age and 2 other fieldsHigh correlation
UO_none is highly overall correlated with Age and 4 other fieldsHigh correlation
UO_student/scholar is highly overall correlated with Age and 2 other fieldsHigh correlation
PW_yeslessthan12h is highly imbalanced (75.5%)Imbalance
HHC_oneperson_with_children is highly imbalanced (63.8%)Imbalance
Ethn_western is highly imbalanced (53.0%)Imbalance
Ethn_nonwestern is highly imbalanced (51.2%)Imbalance
Moti_profession is highly imbalanced (88.2%)Imbalance
Moti_pickupdropoff_person is highly imbalanced (64.9%)Imbalance
Main_moti_profession is highly imbalanced (80.0%)Imbalance
Main_moti_pickupdropoff_person is highly imbalanced (74.9%)Imbalance
Starting_postalcode has 2653 (1.8%) missing valuesMissing
Number_of_cars_in_HH has 23110 (15.7%) zerosZeros

Reproduction

Analysis started2024-07-05 11:46:26.617941
Analysis finished2024-07-05 11:47:06.671013
Duration40.05 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Starting_postalcode
Real number (ℝ)

MISSING 

Distinct3647
Distinct (%)2.5%
Missing2653
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean4357.508
Minimum1011
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-05T13:47:06.913992image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1011
5-th percentile1098
Q12511
median3571
Q36245
95-th percentile8933
Maximum9999
Range8988
Interquartile range (IQR)3734

Descriptive statistics

Standard deviation2439.3285
Coefficient of variation (CV)0.55979898
Kurtosis-0.81882409
Mean4357.508
Median Absolute Deviation (MAD)1691
Skewness0.54234014
Sum6.291065 × 108
Variance5950323.6
MonotonicityNot monotonic
2024-07-05T13:47:07.339222image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3011 827
 
0.6%
2511 544
 
0.4%
3511 503
 
0.3%
3012 472
 
0.3%
2492 422
 
0.3%
1012 419
 
0.3%
2611 413
 
0.3%
2628 359
 
0.2%
3584 346
 
0.2%
2496 331
 
0.2%
Other values (3637) 139737
95.0%
(Missing) 2653
 
1.8%
ValueCountFrequency (%)
1011 209
0.1%
1012 419
0.3%
1013 221
0.2%
1014 87
 
0.1%
1015 137
 
0.1%
1016 153
 
0.1%
1017 294
0.2%
1018 320
0.2%
1019 198
0.1%
1021 48
 
< 0.1%
ValueCountFrequency (%)
9999 1
 
< 0.1%
9998 3
 
< 0.1%
9997 7
 
< 0.1%
9991 10
 
< 0.1%
9989 10
 
< 0.1%
9988 5
 
< 0.1%
9986 1
 
< 0.1%
9983 6
 
< 0.1%
9982 10
 
< 0.1%
9981 33
< 0.1%

Gender_male
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
73679 
0
73347 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters147026
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 73679
50.1%
0 73347
49.9%

Length

2024-07-05T13:47:07.553088image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:07.705450image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 73679
50.1%
0 73347
49.9%

Most occurring characters

ValueCountFrequency (%)
1 73679
50.1%
0 73347
49.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147026
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 73679
50.1%
0 73347
49.9%

Most occurring scripts

ValueCountFrequency (%)
Common 147026
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 73679
50.1%
0 73347
49.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 73679
50.1%
0 73347
49.9%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.613606
Minimum6
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-05T13:47:07.898219image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q126
median43
Q358
95-th percentile75
Maximum99
Range93
Interquartile range (IQR)32

Descriptive statistics

Standard deviation19.854375
Coefficient of variation (CV)0.46591634
Kurtosis-0.92490093
Mean42.613606
Median Absolute Deviation (MAD)16
Skewness0.087448337
Sum6265308
Variance394.19622
MonotonicityNot monotonic
2024-07-05T13:47:08.120842image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 2787
 
1.9%
54 2759
 
1.9%
49 2719
 
1.8%
53 2699
 
1.8%
52 2665
 
1.8%
47 2643
 
1.8%
48 2639
 
1.8%
50 2502
 
1.7%
19 2476
 
1.7%
44 2433
 
1.7%
Other values (84) 120704
82.1%
ValueCountFrequency (%)
6 888
0.6%
7 1102
0.7%
8 1314
0.9%
9 1377
0.9%
10 1424
1.0%
11 1628
1.1%
12 1549
1.1%
13 1506
1.0%
14 1736
1.2%
15 1897
1.3%
ValueCountFrequency (%)
99 2
 
< 0.1%
98 3
 
< 0.1%
97 6
 
< 0.1%
96 2
 
< 0.1%
95 6
 
< 0.1%
94 7
 
< 0.1%
93 12
 
< 0.1%
92 24
< 0.1%
91 50
< 0.1%
90 46
< 0.1%

Number_of_cars_in_HH
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.341409
Minimum0
Maximum10
Zeros23110
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-05T13:47:08.319106image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0141949
Coefficient of variation (CV)0.75606686
Kurtosis15.815971
Mean1.341409
Median Absolute Deviation (MAD)1
Skewness2.3956639
Sum197222
Variance1.0285913
MonotonicityNot monotonic
2024-07-05T13:47:08.504924image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 69241
47.1%
2 42667
29.0%
0 23110
 
15.7%
3 9100
 
6.2%
4 1774
 
1.2%
5 422
 
0.3%
10 399
 
0.3%
6 162
 
0.1%
7 69
 
< 0.1%
8 42
 
< 0.1%
ValueCountFrequency (%)
0 23110
 
15.7%
1 69241
47.1%
2 42667
29.0%
3 9100
 
6.2%
4 1774
 
1.2%
5 422
 
0.3%
6 162
 
0.1%
7 69
 
< 0.1%
8 42
 
< 0.1%
9 40
 
< 0.1%
ValueCountFrequency (%)
10 399
 
0.3%
9 40
 
< 0.1%
8 42
 
< 0.1%
7 69
 
< 0.1%
6 162
 
0.1%
5 422
 
0.3%
4 1774
 
1.2%
3 9100
 
6.2%
2 42667
29.0%
1 69241
47.1%

Ebike_in_HH
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
111554 
1
35472 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters147026
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111554
75.9%
1 35472
 
24.1%

Length

2024-07-05T13:47:08.690613image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:08.851976image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 111554
75.9%
1 35472
 
24.1%

Most occurring characters

ValueCountFrequency (%)
0 111554
75.9%
1 35472
 
24.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147026
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 111554
75.9%
1 35472
 
24.1%

Most occurring scripts

ValueCountFrequency (%)
Common 147026
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 111554
75.9%
1 35472
 
24.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 111554
75.9%
1 35472
 
24.1%
Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.973032
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-05T13:47:09.006543image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median8
Q39
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6574264
Coefficient of variation (CV)0.38110056
Kurtosis-0.5454119
Mean6.973032
Median Absolute Deviation (MAD)2
Skewness-0.69406554
Sum1025217
Variance7.0619151
MonotonicityNot monotonic
2024-07-05T13:47:09.184508image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 29948
20.4%
9 24682
16.8%
8 20402
13.9%
7 17337
11.8%
6 14045
9.6%
5 11439
 
7.8%
4 9573
 
6.5%
3 7251
 
4.9%
1 7184
 
4.9%
2 5165
 
3.5%
ValueCountFrequency (%)
1 7184
 
4.9%
2 5165
 
3.5%
3 7251
 
4.9%
4 9573
 
6.5%
5 11439
 
7.8%
6 14045
9.6%
7 17337
11.8%
8 20402
13.9%
9 24682
16.8%
10 29948
20.4%
ValueCountFrequency (%)
10 29948
20.4%
9 24682
16.8%
8 20402
13.9%
7 17337
11.8%
6 14045
9.6%
5 11439
 
7.8%
4 9573
 
6.5%
3 7251
 
4.9%
2 5165
 
3.5%
1 7184
 
4.9%

Trip_distance
Real number (ℝ)

Distinct1347
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.68033
Minimum0
Maximum3400
Zeros65
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-05T13:47:09.371598image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q110
median30
Q394.75
95-th percentile500
Maximum3400
Range3400
Interquartile range (IQR)84.75

Descriptive statistics

Standard deviation218.98969
Coefficient of variation (CV)2.0527654
Kurtosis30.380952
Mean106.68033
Median Absolute Deviation (MAD)23
Skewness4.6888434
Sum15684782
Variance47956.485
MonotonicityNot monotonic
2024-07-05T13:47:09.620834image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 13343
 
9.1%
20 10576
 
7.2%
30 7735
 
5.3%
5 7618
 
5.2%
50 6708
 
4.6%
40 4920
 
3.3%
15 4814
 
3.3%
1 3396
 
2.3%
100 3365
 
2.3%
60 3010
 
2.0%
Other values (1337) 81541
55.5%
ValueCountFrequency (%)
0 65
 
< 0.1%
1 3396
2.3%
2 2917
 
2.0%
3 3008
 
2.0%
4 2193
 
1.5%
5 7618
5.2%
6 1833
 
1.2%
7 1956
 
1.3%
8 2364
 
1.6%
9 1038
 
0.7%
ValueCountFrequency (%)
3400 2
< 0.1%
3252 1
 
< 0.1%
3070 1
 
< 0.1%
3060 1
 
< 0.1%
3050 3
< 0.1%
3040 1
 
< 0.1%
3000 3
< 0.1%
2950 1
 
< 0.1%
2918 1
 
< 0.1%
2781 1
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1.0
54555 
3.0
45978 
4.0
33724 
2.0
12769 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0 54555
37.1%
3.0 45978
31.3%
4.0 33724
22.9%
2.0 12769
 
8.7%

Length

2024-07-05T13:47:09.838164image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:10.018986image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 54555
37.1%
3.0 45978
31.3%
4.0 33724
22.9%
2.0 12769
 
8.7%

Most occurring characters

ValueCountFrequency (%)
. 147026
33.3%
0 147026
33.3%
1 54555
 
12.4%
3 45978
 
10.4%
4 33724
 
7.6%
2 12769
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147026
50.0%
1 54555
 
18.6%
3 45978
 
15.6%
4 33724
 
11.5%
2 12769
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 147026
33.3%
0 147026
33.3%
1 54555
 
12.4%
3 45978
 
10.4%
4 33724
 
7.6%
2 12769
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 147026
33.3%
0 147026
33.3%
1 54555
 
12.4%
3 45978
 
10.4%
4 33724
 
7.6%
2 12769
 
2.9%

Trip_starthour
Real number (ℝ)

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.475766
Minimum0
Maximum26
Zeros130
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-05T13:47:10.207446image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median14
Q317
95-th percentile21
Maximum26
Range26
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.3488237
Coefficient of variation (CV)0.32271439
Kurtosis-0.74525715
Mean13.475766
Median Absolute Deviation (MAD)3
Skewness0.03455853
Sum1981288
Variance18.912268
MonotonicityNot monotonic
2024-07-05T13:47:10.431693image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
8 13754
 
9.4%
17 13432
 
9.1%
16 12424
 
8.5%
15 11318
 
7.7%
14 11035
 
7.5%
12 9826
 
6.7%
13 9396
 
6.4%
11 9333
 
6.3%
10 9278
 
6.3%
18 8423
 
5.7%
Other values (17) 38807
26.4%
ValueCountFrequency (%)
0 130
 
0.1%
1 194
 
0.1%
2 170
 
0.1%
3 118
 
0.1%
4 146
 
0.1%
5 585
 
0.4%
6 2527
 
1.7%
7 7818
5.3%
8 13754
9.4%
9 8388
5.7%
ValueCountFrequency (%)
26 3
 
< 0.1%
25 12
 
< 0.1%
24 93
 
0.1%
23 1939
 
1.3%
22 2737
 
1.9%
21 3129
 
2.1%
20 4302
 
2.9%
19 6516
4.4%
18 8423
5.7%
17 13432
9.1%

Part_of_sequence
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
130354 
0
16672 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters147026
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 130354
88.7%
0 16672
 
11.3%

Length

2024-07-05T13:47:10.620346image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:10.783821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 130354
88.7%
0 16672
 
11.3%

Most occurring characters

ValueCountFrequency (%)
1 130354
88.7%
0 16672
 
11.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147026
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 130354
88.7%
0 16672
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
Common 147026
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 130354
88.7%
0 16672
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 130354
88.7%
0 16672
 
11.3%

PW_no
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
87471 
1.0
59555 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 87471
59.5%
1.0 59555
40.5%

Length

2024-07-05T13:47:10.967862image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:11.124273image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 87471
59.5%
1.0 59555
40.5%

Most occurring characters

ValueCountFrequency (%)
0 234497
53.2%
. 147026
33.3%
1 59555
 
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234497
79.7%
1 59555
 
20.3%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 234497
53.2%
. 147026
33.3%
1 59555
 
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 234497
53.2%
. 147026
33.3%
1 59555
 
13.5%

PW_yeslessthan12h
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
141063 
1.0
 
5963

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 141063
95.9%
1.0 5963
 
4.1%

Length

2024-07-05T13:47:11.293184image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:11.454943image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 141063
95.9%
1.0 5963
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 288089
65.3%
. 147026
33.3%
1 5963
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 288089
98.0%
1 5963
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 288089
65.3%
. 147026
33.3%
1 5963
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 288089
65.3%
. 147026
33.3%
1 5963
 
1.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
125292 
1.0
21734 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 125292
85.2%
1.0 21734
 
14.8%

Length

2024-07-05T13:47:11.621596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:11.764917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 125292
85.2%
1.0 21734
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 272318
61.7%
. 147026
33.3%
1 21734
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 272318
92.6%
1 21734
 
7.4%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 272318
61.7%
. 147026
33.3%
1 21734
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 272318
61.7%
. 147026
33.3%
1 21734
 
4.9%

PW_yesmorethan30h
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
87252 
1.0
59774 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 87252
59.3%
1.0 59774
40.7%

Length

2024-07-05T13:47:11.951451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:12.109220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 87252
59.3%
1.0 59774
40.7%

Most occurring characters

ValueCountFrequency (%)
0 234278
53.1%
. 147026
33.3%
1 59774
 
13.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234278
79.7%
1 59774
 
20.3%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 234278
53.1%
. 147026
33.3%
1 59774
 
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 234278
53.1%
. 147026
33.3%
1 59774
 
13.6%

UO_none
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
75899 
1.0
71127 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 75899
51.6%
1.0 71127
48.4%

Length

2024-07-05T13:47:12.282451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:12.438380image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 75899
51.6%
1.0 71127
48.4%

Most occurring characters

ValueCountFrequency (%)
0 222925
50.5%
. 147026
33.3%
1 71127
 
16.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 222925
75.8%
1 71127
 
24.2%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 222925
50.5%
. 147026
33.3%
1 71127
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 222925
50.5%
. 147026
33.3%
1 71127
 
16.1%

UO_benefits
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
103594 
1.0
43432 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 103594
70.5%
1.0 43432
29.5%

Length

2024-07-05T13:47:12.621825image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:12.809602image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 103594
70.5%
1.0 43432
29.5%

Most occurring characters

ValueCountFrequency (%)
0 250620
56.8%
. 147026
33.3%
1 43432
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 250620
85.2%
1 43432
 
14.8%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 250620
56.8%
. 147026
33.3%
1 43432
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 250620
56.8%
. 147026
33.3%
1 43432
 
9.8%

UO_student/scholar
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
114559 
1.0
32467 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 114559
77.9%
1.0 32467
 
22.1%

Length

2024-07-05T13:47:12.998514image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:13.180388image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 114559
77.9%
1.0 32467
 
22.1%

Most occurring characters

ValueCountFrequency (%)
0 261585
59.3%
. 147026
33.3%
1 32467
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 261585
89.0%
1 32467
 
11.0%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 261585
59.3%
. 147026
33.3%
1 32467
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 261585
59.3%
. 147026
33.3%
1 32467
 
7.4%

HHC_oneperson
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
120576 
1.0
26450 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 120576
82.0%
1.0 26450
 
18.0%

Length

2024-07-05T13:47:13.349104image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:13.504740image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 120576
82.0%
1.0 26450
 
18.0%

Most occurring characters

ValueCountFrequency (%)
0 267602
60.7%
. 147026
33.3%
1 26450
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 267602
91.0%
1 26450
 
9.0%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 267602
60.7%
. 147026
33.3%
1 26450
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 267602
60.7%
. 147026
33.3%
1 26450
 
6.0%

HHC_couple
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
104334 
1.0
42692 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 104334
71.0%
1.0 42692
29.0%

Length

2024-07-05T13:47:13.694159image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:13.909944image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 104334
71.0%
1.0 42692
29.0%

Most occurring characters

ValueCountFrequency (%)
0 251360
57.0%
. 147026
33.3%
1 42692
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 251360
85.5%
1 42692
 
14.5%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 251360
57.0%
. 147026
33.3%
1 42692
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 251360
57.0%
. 147026
33.3%
1 42692
 
9.7%

HHC_couple_with_children
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
79280 
1.0
67746 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 79280
53.9%
1.0 67746
46.1%

Length

2024-07-05T13:47:14.125693image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:14.282556image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 79280
53.9%
1.0 67746
46.1%

Most occurring characters

ValueCountFrequency (%)
0 226306
51.3%
. 147026
33.3%
1 67746
 
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 226306
77.0%
1 67746
 
23.0%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 226306
51.3%
. 147026
33.3%
1 67746
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 226306
51.3%
. 147026
33.3%
1 67746
 
15.4%

HHC_oneperson_with_children
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
136888 
1.0
 
10138

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 136888
93.1%
1.0 10138
 
6.9%

Length

2024-07-05T13:47:14.505181image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:14.670548image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 136888
93.1%
1.0 10138
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0 283914
64.4%
. 147026
33.3%
1 10138
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 283914
96.6%
1 10138
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 283914
64.4%
. 147026
33.3%
1 10138
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 283914
64.4%
. 147026
33.3%
1 10138
 
2.3%

Ethn_dutch
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1.0
116667 
0.0
30359 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 116667
79.4%
0.0 30359
 
20.6%

Length

2024-07-05T13:47:14.886708image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:15.048440image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 116667
79.4%
0.0 30359
 
20.6%

Most occurring characters

ValueCountFrequency (%)
0 177385
40.2%
. 147026
33.3%
1 116667
26.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 177385
60.3%
1 116667
39.7%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 177385
40.2%
. 147026
33.3%
1 116667
26.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 177385
40.2%
. 147026
33.3%
1 116667
26.5%

Ethn_western
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
132277 
1.0
14749 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 132277
90.0%
1.0 14749
 
10.0%

Length

2024-07-05T13:47:15.218220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:15.438102image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 132277
90.0%
1.0 14749
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 279303
63.3%
. 147026
33.3%
1 14749
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 279303
95.0%
1 14749
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 279303
63.3%
. 147026
33.3%
1 14749
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 279303
63.3%
. 147026
33.3%
1 14749
 
3.3%

Ethn_nonwestern
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
131416 
1.0
15610 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 131416
89.4%
1.0 15610
 
10.6%

Length

2024-07-05T13:47:15.626936image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:15.783192image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 131416
89.4%
1.0 15610
 
10.6%

Most occurring characters

ValueCountFrequency (%)
0 278442
63.1%
. 147026
33.3%
1 15610
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 278442
94.7%
1 15610
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 278442
63.1%
. 147026
33.3%
1 15610
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 278442
63.1%
. 147026
33.3%
1 15610
 
3.5%

EL_primary
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
125867 
1.0
21159 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 125867
85.6%
1.0 21159
 
14.4%

Length

2024-07-05T13:47:16.013525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:16.184445image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 125867
85.6%
1.0 21159
 
14.4%

Most occurring characters

ValueCountFrequency (%)
0 272893
61.9%
. 147026
33.3%
1 21159
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 272893
92.8%
1 21159
 
7.2%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 272893
61.9%
. 147026
33.3%
1 21159
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 272893
61.9%
. 147026
33.3%
1 21159
 
4.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
129108 
1.0
17918 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 129108
87.8%
1.0 17918
 
12.2%

Length

2024-07-05T13:47:16.345313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:16.538520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 129108
87.8%
1.0 17918
 
12.2%

Most occurring characters

ValueCountFrequency (%)
0 276134
62.6%
. 147026
33.3%
1 17918
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 276134
93.9%
1 17918
 
6.1%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 276134
62.6%
. 147026
33.3%
1 17918
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 276134
62.6%
. 147026
33.3%
1 17918
 
4.1%

EL_secondary_higher
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
102895 
1.0
44131 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 102895
70.0%
1.0 44131
30.0%

Length

2024-07-05T13:47:16.757833image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:16.913852image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 102895
70.0%
1.0 44131
30.0%

Most occurring characters

ValueCountFrequency (%)
0 249921
56.7%
. 147026
33.3%
1 44131
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 249921
85.0%
1 44131
 
15.0%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 249921
56.7%
. 147026
33.3%
1 44131
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 249921
56.7%
. 147026
33.3%
1 44131
 
10.0%

EL_higherprofessional_university
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
83208 
1.0
63818 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 83208
56.6%
1.0 63818
43.4%

Length

2024-07-05T13:47:17.100499image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:17.268753image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 83208
56.6%
1.0 63818
43.4%

Most occurring characters

ValueCountFrequency (%)
0 230234
52.2%
. 147026
33.3%
1 63818
 
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 230234
78.3%
1 63818
 
21.7%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 230234
52.2%
. 147026
33.3%
1 63818
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 230234
52.2%
. 147026
33.3%
1 63818
 
14.5%

Moti_work
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
112531 
1.0
34495 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 112531
76.5%
1.0 34495
 
23.5%

Length

2024-07-05T13:47:17.482316image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:17.654132image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 112531
76.5%
1.0 34495
 
23.5%

Most occurring characters

ValueCountFrequency (%)
0 259557
58.8%
. 147026
33.3%
1 34495
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 259557
88.3%
1 34495
 
11.7%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 259557
58.8%
. 147026
33.3%
1 34495
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 259557
58.8%
. 147026
33.3%
1 34495
 
7.8%

Moti_profession
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
144684 
1.0
 
2342

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 144684
98.4%
1.0 2342
 
1.6%

Length

2024-07-05T13:47:17.892045image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:18.102783image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 144684
98.4%
1.0 2342
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 291710
66.1%
. 147026
33.3%
1 2342
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 291710
99.2%
1 2342
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 291710
66.1%
. 147026
33.3%
1 2342
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 291710
66.1%
. 147026
33.3%
1 2342
 
0.5%

Moti_pickupdropoff_person
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
137301 
1.0
 
9725

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 137301
93.4%
1.0 9725
 
6.6%

Length

2024-07-05T13:47:18.293911image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:18.455177image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 137301
93.4%
1.0 9725
 
6.6%

Most occurring characters

ValueCountFrequency (%)
0 284327
64.5%
. 147026
33.3%
1 9725
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 284327
96.7%
1 9725
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 284327
64.5%
. 147026
33.3%
1 9725
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 284327
64.5%
. 147026
33.3%
1 9725
 
2.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
114051 
1.0
32975 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 114051
77.6%
1.0 32975
 
22.4%

Length

2024-07-05T13:47:18.638491image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:18.797514image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 114051
77.6%
1.0 32975
 
22.4%

Most occurring characters

ValueCountFrequency (%)
0 261077
59.2%
. 147026
33.3%
1 32975
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 261077
88.8%
1 32975
 
11.2%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 261077
59.2%
. 147026
33.3%
1 32975
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 261077
59.2%
. 147026
33.3%
1 32975
 
7.5%

Moti_sparetime
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
79537 
1.0
67489 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 79537
54.1%
1.0 67489
45.9%

Length

2024-07-05T13:47:19.029749image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:19.200431image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 79537
54.1%
1.0 67489
45.9%

Most occurring characters

ValueCountFrequency (%)
0 226563
51.4%
. 147026
33.3%
1 67489
 
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 226563
77.0%
1 67489
 
23.0%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 226563
51.4%
. 147026
33.3%
1 67489
 
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 226563
51.4%
. 147026
33.3%
1 67489
 
15.3%

Main_moti_work
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
103706 
1.0
43320 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 103706
70.5%
1.0 43320
29.5%

Length

2024-07-05T13:47:19.432665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:19.638374image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 103706
70.5%
1.0 43320
29.5%

Most occurring characters

ValueCountFrequency (%)
0 250732
56.8%
. 147026
33.3%
1 43320
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 250732
85.3%
1 43320
 
14.7%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 250732
56.8%
. 147026
33.3%
1 43320
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 250732
56.8%
. 147026
33.3%
1 43320
 
9.8%

Main_moti_profession
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
142443 
1.0
 
4583

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 142443
96.9%
1.0 4583
 
3.1%

Length

2024-07-05T13:47:19.819669image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:20.028199image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 142443
96.9%
1.0 4583
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 289469
65.6%
. 147026
33.3%
1 4583
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 289469
98.4%
1 4583
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 289469
65.6%
. 147026
33.3%
1 4583
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 289469
65.6%
. 147026
33.3%
1 4583
 
1.0%

Main_moti_pickupdropoff_person
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
140868 
1.0
 
6158

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 140868
95.8%
1.0 6158
 
4.2%

Length

2024-07-05T13:47:20.233005image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:20.380148image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 140868
95.8%
1.0 6158
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 287894
65.3%
. 147026
33.3%
1 6158
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 287894
97.9%
1 6158
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 287894
65.3%
. 147026
33.3%
1 6158
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 287894
65.3%
. 147026
33.3%
1 6158
 
1.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
115747 
1.0
31279 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 115747
78.7%
1.0 31279
 
21.3%

Length

2024-07-05T13:47:20.555123image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:20.720682image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 115747
78.7%
1.0 31279
 
21.3%

Most occurring characters

ValueCountFrequency (%)
0 262773
59.6%
. 147026
33.3%
1 31279
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 262773
89.4%
1 31279
 
10.6%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 262773
59.6%
. 147026
33.3%
1 31279
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 262773
59.6%
. 147026
33.3%
1 31279
 
7.1%

Main_moti_sparetime
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.0
85340 
1.0
61686 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441078
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 85340
58.0%
1.0 61686
42.0%

Length

2024-07-05T13:47:20.896567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:47:21.050313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 85340
58.0%
1.0 61686
42.0%

Most occurring characters

ValueCountFrequency (%)
0 232366
52.7%
. 147026
33.3%
1 61686
 
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294052
66.7%
Other Punctuation 147026
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 232366
79.0%
1 61686
 
21.0%
Other Punctuation
ValueCountFrequency (%)
. 147026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 232366
52.7%
. 147026
33.3%
1 61686
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 232366
52.7%
. 147026
33.3%
1 61686
 
14.0%

Interactions

2024-07-05T13:47:02.215070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:55.837926image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:56.967048image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:58.211664image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:59.632663image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:00.749021image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:02.915155image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:56.017205image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:57.150180image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:58.641169image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:59.799572image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:01.081908image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:03.081586image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:56.183550image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:57.338204image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:58.816307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:59.989129image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:01.348785image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:03.247912image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:56.378644image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:57.535375image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:59.016114image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:00.189222image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:01.539052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:03.438449image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:56.572080image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:57.716292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:59.199620image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:00.371431image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:01.742694image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:03.654062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:56.783739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:57.900047image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:46:59.405010image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:00.570508image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:47:01.998477image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-07-05T13:47:21.213062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
AgeDisposable_income_householdEL_higherprofessional_universityEL_primaryEL_secondary_higherEL_secondary_lowerEbike_in_HHEthn_dutchEthn_nonwesternEthn_westernGender_maleHHC_coupleHHC_couple_with_childrenHHC_onepersonHHC_oneperson_with_childrenMain_moti_pickupdropoff_goodsMain_moti_pickupdropoff_personMain_moti_professionMain_moti_sparetimeMain_moti_workMoti_pickupdropoff_goodsMoti_pickupdropoff_personMoti_professionMoti_sparetimeMoti_workNumber_of_cars_in_HHPW_noPW_yeslessthan12hPW_yesmorethan12to30hPW_yesmorethan30hPart_of_sequenceStarting_postalcodeTrip_distanceTrip_starthourTrip_transportation_typeUO_benefitsUO_noneUO_student/scholar
Age1.000-0.1250.3960.7760.2810.2890.2620.1340.1600.0510.0820.5090.5110.2190.1780.0520.0470.0220.0970.0790.1870.1990.0640.3430.270-0.0240.6870.2230.2230.5610.0900.0270.122-0.0320.2320.6570.6500.883
Disposable_income_household-0.1251.0000.1670.0570.0820.1950.0990.0980.0910.0510.0400.1510.4450.4780.1760.0270.0160.0200.0170.0200.0880.0640.0190.0370.0630.4380.2000.0490.0840.1770.030-0.0490.0600.0020.0810.2650.1940.177
EL_higherprofessional_university0.3960.1671.0000.3590.5740.3260.0890.0040.0400.0480.0070.0700.0800.0720.0760.0170.0060.0060.0410.0590.0110.0530.0010.1370.142-0.0270.2960.0810.0190.3140.009-0.0810.0740.0380.0900.0720.3210.308
EL_primary0.7760.0570.3591.0000.2680.1530.0300.0780.0990.0010.0050.1690.2020.1170.0840.0150.0110.0010.0570.0430.0750.0700.0290.2390.1580.0180.3730.0080.1390.2750.0080.005-0.163-0.0290.2650.1160.3250.520
EL_secondary_higher0.2810.0820.5740.2681.0000.2440.0340.0260.0100.0250.0060.0370.0130.0090.0260.0110.0000.0000.0040.0170.0240.0100.0220.0290.0000.0410.0640.0600.0990.0310.0000.0520.0410.0050.0950.0000.0000.000
EL_secondary_lower0.2890.1950.3260.1530.2441.0000.1190.0540.0310.0410.0070.1280.1130.0020.0120.0260.0000.0100.0050.0200.0640.0180.0030.0080.044-0.0350.1380.0290.0190.1360.0040.0450.006-0.0330.0280.2310.1390.087
Ebike_in_HH0.2620.0990.0890.0300.0340.1191.0000.1380.1200.0620.0200.1580.0220.1210.0570.0180.0040.0080.0040.0220.0270.0070.0020.0220.0480.1100.0920.0010.0040.0960.0230.0840.051-0.0220.0880.1670.0910.074
Ethn_dutch0.1340.0980.0040.0780.0260.0540.1381.0000.6760.6550.0190.0720.0080.0310.0660.0060.0000.0110.0100.0100.0020.0030.0120.0100.0160.1480.0120.0030.0340.0110.0510.1760.040-0.0000.1440.0430.0150.066
Ethn_nonwestern0.1600.0910.0400.0990.0100.0310.1200.6761.0000.1150.0000.0960.0420.0040.0810.0030.0040.0110.0070.0110.0100.0040.0090.0000.014-0.1140.0260.0170.0250.0150.052-0.148-0.037-0.0020.1480.0550.0380.106
Ethn_western0.0510.0510.0480.0010.0250.0410.0620.6550.1151.0000.0240.0000.0320.0360.0050.0030.0060.0030.0060.0000.0060.0110.0070.0150.007-0.0830.0100.0110.0200.0300.016-0.085-0.0150.0020.0430.0000.0180.020
Gender_male0.0820.0400.0070.0050.0060.0070.0200.0190.0000.0241.0000.0660.0150.0180.0610.0000.0140.0110.0100.0150.0370.0550.0310.0060.0670.0240.0150.0630.2710.2360.0020.0080.1170.0080.1010.0390.0590.028
HHC_couple0.5090.1510.0700.1690.0370.1280.1580.0720.0960.0000.0661.0000.5910.3000.1740.0230.0140.0000.0140.0000.0840.0760.0110.0420.008-0.0430.0750.0570.0740.0000.0020.0280.066-0.0040.0840.2900.0280.284
HHC_couple_with_children0.5110.4450.0800.2020.0130.1130.0220.0080.0420.0320.0150.5911.0000.4330.2520.0250.0300.0030.0110.0040.1000.1310.0030.0290.0110.4070.0980.0520.1110.0030.0220.022-0.031-0.0330.0780.2780.0600.233
HHC_oneperson0.2190.4780.0720.1170.0090.0020.1210.0310.0040.0360.0180.3000.4331.0000.1270.0100.0210.0100.0060.0110.0420.0810.0050.0050.015-0.4050.0230.0130.0760.0360.024-0.040-0.0180.0430.0840.0690.0140.059
HHC_oneperson_with_children0.1780.1760.0760.0840.0260.0120.0570.0660.0810.0050.0610.1740.2520.1271.0000.0070.0000.0030.0130.0070.0160.0000.0000.0280.016-0.1100.0220.0190.0280.0500.013-0.032-0.0300.0070.0680.0770.0460.140
Main_moti_pickupdropoff_goods0.0520.0270.0170.0150.0110.0260.0180.0060.0030.0030.0000.0230.0250.0100.0071.0000.1090.0930.4420.3360.1320.0130.0080.0510.060-0.0180.0260.0120.0060.0260.0000.027-0.014-0.0280.0280.0480.0190.030
Main_moti_pickupdropoff_person0.0470.0160.0060.0110.0000.0000.0040.0000.0040.0060.0140.0140.0300.0210.0000.1091.0000.0370.1780.1350.0090.1280.0030.0330.0250.0080.0110.0030.0210.0060.0060.017-0.008-0.0170.0310.0170.0080.029
Main_moti_profession0.0220.0200.0060.0010.0000.0100.0080.0110.0110.0030.0110.0000.0030.0100.0030.0930.0371.0000.1520.1160.0050.0030.1250.0120.0130.0130.0120.0080.0000.0130.0050.0080.009-0.0050.0280.0000.0130.018
Main_moti_sparetime0.0970.0170.0410.0570.0040.0050.0040.0100.0070.0060.0100.0140.0110.0060.0130.4420.1780.1521.0000.5490.0520.0280.0230.1370.0860.0180.0670.0380.0170.0700.0000.009-0.0120.0000.0300.0050.0700.091
Main_moti_work0.0790.0200.0590.0430.0170.0200.0220.0100.0110.0000.0150.0000.0040.0110.0070.3360.1350.1160.5491.0000.0550.0120.0130.0830.163-0.0130.0870.0290.0010.0960.006-0.0450.0250.0350.0290.0450.0840.052
Moti_pickupdropoff_goods0.1870.0880.0110.0750.0240.0640.0270.0020.0100.0060.0370.0840.1000.0420.0160.1320.0090.0050.0520.0551.0000.1430.0680.4950.298-0.0500.0730.0140.0070.0720.0810.012-0.1840.0060.0850.1730.0530.127
Moti_pickupdropoff_person0.1990.0640.0530.0700.0100.0180.0070.0030.0040.0110.0550.0760.1310.0810.0000.0130.1280.0030.0280.0120.1431.0000.0340.2450.1470.0530.0590.0060.0940.0050.0430.008-0.037-0.0270.1360.0480.0420.104
Moti_profession0.0640.0190.0010.0290.0220.0030.0020.0120.0090.0070.0310.0110.0030.0050.0000.0080.0030.1250.0230.0130.0680.0341.0000.1170.0700.0300.0750.0060.0220.0560.0230.0030.0590.0080.0760.0290.0670.048
Moti_sparetime0.3430.0370.1370.2390.0290.0080.0220.0100.0000.0150.0060.0420.0290.0050.0280.0510.0330.0120.1370.0830.4950.2450.1171.0000.510-0.0130.2730.0480.0850.2300.0080.024-0.0330.1480.2140.0000.2620.316
Moti_work0.2700.0630.1420.1580.0000.0440.0480.0160.0140.0070.0670.0080.0110.0150.0160.0600.0250.0130.0860.1630.2980.1470.0700.5101.0000.0250.3360.0410.0320.3290.102-0.0460.224-0.1670.1640.1890.3150.171
Number_of_cars_in_HH-0.0240.438-0.0270.0180.041-0.0350.1100.148-0.114-0.0830.024-0.0430.407-0.405-0.110-0.0180.0080.0130.018-0.013-0.0500.0530.030-0.0130.0251.0000.1360.0230.0750.0800.0440.1250.147-0.0170.1400.1440.1150.047
PW_no0.6870.2000.2960.3730.0640.1380.0920.0120.0260.0100.0150.0750.0980.0230.0220.0260.0110.0120.0670.0870.0730.0590.0750.2730.3360.1361.0000.1700.3440.6830.0020.039-0.165-0.0330.2260.5050.7990.407
PW_yeslessthan12h0.2230.0490.0810.0080.0600.0290.0010.0030.0170.0110.0630.0570.0520.0130.0190.0120.0030.0080.0380.0290.0140.0060.0060.0480.0410.0230.1701.0000.0860.1700.0150.012-0.0230.0100.0740.0180.1320.180
PW_yesmorethan12to30h0.2230.0840.0190.1390.0990.0190.0040.0340.0250.0200.2710.0740.1110.0760.0280.0060.0210.0000.0170.0010.0070.0940.0220.0850.0320.0750.3440.0861.0000.3450.0200.040-0.017-0.0060.0520.1020.1810.106
PW_yesmorethan30h0.5610.1770.3140.2750.0310.1360.0960.0110.0150.0300.2360.0000.0030.0360.0500.0260.0060.0130.0700.0960.0720.0050.0560.2300.3290.0800.6830.1700.3451.0000.004-0.0720.1860.0330.2180.4240.7210.402
Part_of_sequence0.0900.0300.0090.0080.0000.0040.0230.0510.0520.0160.0020.0020.0220.0240.0130.0000.0060.0050.0000.0060.0810.0430.0230.0080.1020.0440.0020.0150.0200.0041.0000.037-0.0360.3260.2520.0340.0110.051
Starting_postalcode0.027-0.049-0.0810.0050.0520.0450.0840.176-0.148-0.0850.0080.0280.022-0.040-0.0320.0270.0170.0080.009-0.0450.0120.0080.0030.024-0.0460.1250.0390.0120.040-0.0720.0371.0000.040-0.0260.1070.0570.0450.036
Trip_distance0.1220.0600.074-0.1630.0410.0060.0510.040-0.037-0.0150.1170.066-0.031-0.018-0.030-0.014-0.0080.009-0.0120.025-0.184-0.0370.059-0.0330.2240.147-0.165-0.023-0.0170.186-0.0360.0401.000-0.0430.1800.0540.1170.082
Trip_starthour-0.0320.0020.038-0.0290.005-0.033-0.022-0.000-0.0020.0020.008-0.004-0.0330.0430.007-0.028-0.017-0.0050.0000.0350.006-0.0270.0080.148-0.167-0.017-0.0330.010-0.0060.0330.326-0.026-0.0431.0000.0720.1940.1890.067
Trip_transportation_type0.2320.0810.0900.2650.0950.0280.0880.1440.1480.0430.1010.0840.0780.0840.0680.0280.0310.0280.0300.0290.0850.1360.0760.2140.1640.1400.2260.0740.0520.2180.2520.1070.1800.0721.0000.1030.2290.338
UO_benefits0.6570.2650.0720.1160.0000.2310.1670.0430.0550.0000.0390.2900.2780.0690.0770.0480.0170.0000.0050.0450.1730.0480.0290.0000.1890.1440.5050.0180.1020.4240.0340.0570.0540.1940.1031.0000.6270.345
UO_none0.6500.1940.3210.3250.0000.1390.0910.0150.0380.0180.0590.0280.0600.0140.0460.0190.0080.0130.0700.0840.0530.0420.0670.2620.3150.1150.7990.1320.1810.7210.0110.0450.1170.1890.2290.6271.0000.515
UO_student/scholar0.8830.1770.3080.5200.0000.0870.0740.0660.1060.0200.0280.2840.2330.0590.1400.0300.0290.0180.0910.0520.1270.1040.0480.3160.1710.0470.4070.1800.1060.4020.0510.0360.0820.0670.3380.3450.5151.000

Missing values

2024-07-05T13:47:03.987810image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-05T13:47:05.133059image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Starting_postalcodeGender_maleAgeNumber_of_cars_in_HHEbike_in_HHDisposable_income_householdTrip_distanceTrip_transportation_typeTrip_starthourPart_of_sequencePW_noPW_yeslessthan12hPW_yesmorethan12to30hPW_yesmorethan30hUO_noneUO_benefitsUO_student/scholarHHC_onepersonHHC_coupleHHC_couple_with_childrenHHC_oneperson_with_childrenEthn_dutchEthn_westernEthn_nonwesternEL_primaryEL_secondary_lowerEL_secondary_higherEL_higherprofessional_universityMoti_workMoti_professionMoti_pickupdropoff_personMoti_pickupdropoff_goodsMoti_sparetimeMain_moti_workMain_moti_professionMain_moti_pickupdropoff_personMain_moti_pickupdropoff_goodsMain_moti_sparetime
08442.013310325.01.08.000.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.01.0
18443.013310325.01.08.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.01.0
29203.001210530.03.010.011.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.00.01.0
39203.001210530.03.011.011.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.00.01.0
49203.001210530.03.013.011.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.01.0
59203.001210530.03.014.011.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.01.0
67323.0050201020.01.09.010.00.01.00.01.00.00.00.01.00.00.00.01.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
77323.0050201020.01.010.010.00.01.00.01.00.00.00.01.00.00.00.01.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
86711.005911840.04.08.011.00.00.00.00.01.00.00.00.01.00.01.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.01.0
96711.005911830.04.013.011.00.00.00.00.01.00.00.00.01.00.01.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.01.0
Starting_postalcodeGender_maleAgeNumber_of_cars_in_HHEbike_in_HHDisposable_income_householdTrip_distanceTrip_transportation_typeTrip_starthourPart_of_sequencePW_noPW_yeslessthan12hPW_yesmorethan12to30hPW_yesmorethan30hUO_noneUO_benefitsUO_student/scholarHHC_onepersonHHC_coupleHHC_couple_with_childrenHHC_oneperson_with_childrenEthn_dutchEthn_westernEthn_nonwesternEL_primaryEL_secondary_lowerEL_secondary_higherEL_higherprofessional_universityMoti_workMoti_professionMoti_pickupdropoff_personMoti_pickupdropoff_goodsMoti_sparetimeMain_moti_workMain_moti_professionMain_moti_pickupdropoff_personMain_moti_pickupdropoff_goodsMain_moti_sparetime
1470163411.01553110350.01.016.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.01.0
1470173011.0155311030.04.018.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.01.0
1470183032.0155311030.04.023.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.01.0
1470193417.002410835.04.011.010.00.00.01.01.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.00.01.00.00.00.00.01.0
1470203961.006531930.04.014.010.01.00.00.00.01.00.00.01.00.00.00.01.00.00.01.00.00.00.00.00.00.01.00.00.00.00.01.0
1470213833.01620913.03.013.011.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.01.0
1470223831.01620912.04.013.011.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.01.0
1470233524.012500195.03.08.000.00.00.01.01.00.00.01.00.00.00.01.00.00.00.00.00.01.01.00.00.00.00.00.00.00.00.01.0
1470243542.012500195.03.018.010.00.00.01.01.00.00.01.00.00.00.01.00.00.00.00.00.01.01.00.00.00.00.01.00.00.00.00.0
1470253524.012500165.04.019.010.00.00.01.01.00.00.01.00.00.00.01.00.00.00.00.00.01.00.00.00.00.01.01.00.00.00.00.0

Duplicate rows

Most frequently occurring

Starting_postalcodeGender_maleAgeNumber_of_cars_in_HHEbike_in_HHDisposable_income_householdTrip_distanceTrip_transportation_typeTrip_starthourPart_of_sequencePW_noPW_yeslessthan12hPW_yesmorethan12to30hPW_yesmorethan30hUO_noneUO_benefitsUO_student/scholarHHC_onepersonHHC_coupleHHC_couple_with_childrenHHC_oneperson_with_childrenEthn_dutchEthn_westernEthn_nonwesternEL_primaryEL_secondary_lowerEL_secondary_higherEL_higherprofessional_universityMoti_workMoti_professionMoti_pickupdropoff_personMoti_pickupdropoff_goodsMoti_sparetimeMain_moti_workMain_moti_professionMain_moti_pickupdropoff_personMain_moti_pickupdropoff_goodsMain_moti_sparetime# duplicates
7652273.017810410.01.013.001.00.00.00.00.01.00.00.01.00.00.01.00.00.00.01.00.00.00.00.00.01.00.00.00.01.00.00.010
8192331.01810103.03.08.001.00.00.00.00.00.01.00.00.01.00.00.01.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.010
13012953.01491081.01.010.000.00.00.01.01.00.00.00.00.01.00.01.00.00.00.01.00.00.00.00.00.01.00.01.00.00.00.00.08
18703781.007210101.04.017.001.00.00.00.00.01.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.00.01.00.00.00.00.01.08
20354251.0081071.03.08.001.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.00.01.08
4611689.01610033.04.014.000.00.00.01.01.00.00.00.01.00.00.00.00.01.00.01.00.00.00.00.00.01.00.01.00.00.00.00.07
5261827.00742082.03.011.011.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.00.00.00.01.06
3401441.00721058.03.011.011.00.00.00.00.01.00.00.01.00.00.01.00.00.00.01.00.00.00.00.00.01.00.01.00.00.00.00.05
6392035.01120031.04.014.001.00.00.00.00.00.01.00.00.01.00.00.00.01.01.00.00.00.00.00.00.01.00.00.00.00.01.00.05
8182331.01810101.04.012.011.00.00.00.00.00.01.00.00.01.00.00.01.00.01.00.00.00.00.00.00.00.01.00.00.00.00.01.05